TY - JOUR
T1 - A decision support system for assessing management interventions in a mental health ecosystem
T2 - The case of Bizkaia (Basque Country, Spain)
AU - García-Alonso, Carlos R.
AU - Almeda, Nerea
AU - Salinas-Pérez, José A.
AU - Gutiérrez-Colosía, Mencía R.
AU - Uriarte-Uriarte, José J.
AU - Salvador-Carulla, Luis
N1 - Funding Information:
The present research study is frameworked in the REFINEMENT Spain project (Project PI15/01986), funded by the Carlos III Health Institute (http://www.isciii.es/). No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would like to thank the managers of the MH system of Bizkaia, particularly Carlos Pereira, Enrique Pinilla and José Uriarte, and the planners of the MH system of Gipuzkoa, highlighting Álvaro Iruin and Andrea Gabilondo, for the provision of data and support to develop the present study. The present research study is frame-worked in the REFINEMENT Spain project (Project PI15/01986), Carlos III Health Institute (Health Ministry).
Publisher Copyright:
© 2019 García-Alonso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2019/2
Y1 - 2019/2
N2 - Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/ policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system.
AB - Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/ policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system.
UR - http://www.scopus.com/inward/record.url?scp=85061531881&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0212179
DO - 10.1371/journal.pone.0212179
M3 - Article
C2 - 30763361
AN - SCOPUS:85061531881
SN - 1932-6203
VL - 14
SP - 1
EP - 26
JO - PLoS One
JF - PLoS One
IS - 2
M1 - e0212179
ER -
